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The Great Compression: Geopolitical Fragmentation, AI, and the Coming Neo-Feudal Order

Submitted:

01 April 2026

Posted:

02 April 2026

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Abstract
The post-World War II international order is undergoing simultaneous collapse on two fronts: a geopolitical fragmentation driven by twenty consecutive years of democratic decline, and an accelerating concentration of economic power driven by advances in artificial intelligence. This paper argues that the convergence of these two forces is producing a structural transformation unprecedented in human history, one that could stabilize into a neo-feudal equilibrium in which a vanishingly small class of infrastructure owners wields power comparable to pre-Enlightenment monarchs, while the vast majority of humanity loses both its labor value and its political leverage. Unlike previous feudal orders, this one may prove uniquely resistant to revolution, because the mechanisms of enforcement (autonomous weapons, AI surveillance, algorithmic propaganda) do not require human cooperation and therefore cannot be undermined by human dissent. The paper examines the historical parallels (and crucial disanalogies) between contemporary populist-authoritarian movements and their twentieth-century predecessors, models the emerging class structure under conditions of artificial general intelligence, evaluates Universal Basic Income through the lens of incentive structure, arguing that without the revolutionary threat that historically forced redistribution, UBI will default to a pacification mechanism rather than a genuine solution, examines the future of the nation-state under conditions where AI infrastructure owners command more wealth and capability than most governments, and argues that the effective altruism community's near-exclusive focus on existential risk from AI has created a dangerous blind spot around the political economy of who controls AI and who benefits from it.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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